Demystifying AI Agents: What They Are and How Marketers Can Use Them

If the phrase “AI agent” still feels a little fuzzy, you’re not alone.

You may know that AI agents are a big deal, and that they can help your business. Unfortunately, that doesn’t mean a whole lot if you don’t know how to take this trendy term and apply it to your business. 

The rapid growth and complexity of AI can make the whole subject extremely intimidating, especially for marketers or business leaders who already have plenty on their proverbial plates. There’s a lot of pressure to keep up, but that barrier to entry drives a lot of people away instead of allowing them to explore the possibilities AI agents provide.

The good news is that you don’t need to become an AI engineer to benefit from agents. What it does take is an understanding of what “AI agent” really means so that you can compare different approaches and uncover what they can help you do to help you.

First, What Do We Actually Mean by AI?

The term “AI” is used very loosely these days, and that plays a major role in why concepts like AI agents are confusing. Artificial intelligence isn’t a new concept, and it definitely didn’t start with ChatGPT. 

AI has been discussed and developed for decades. What has changed more recently is the rise of large language models (LLMs) and the tools built around them. In everyday conversation, “AI” is now often used as shorthand for products powered by LLMs like ChatGPT and Google’s AI overviews, even though those tools represent only one part of a much broader field.

For the purposes of this article, that’s the part of AI we care about. An LLM is the underlying model that powers many of the AI tools people use today. But the model itself is only one piece of the puzzle.

When you ask an AI tool to search the web, review a document, compare information, or interact with another system, the model isn’t necessarily performing all those actions by itself. It may be using other tools that have been connected to it. That distinction becomes important when we start talking about agents.

So, What Is an AI Agent?

It’s best not to think of an AI agent as a specific tool, like Claude or something similar. That example is way too specific. Instead, a useful way to think about an AI agent is to consider what they do:

An AI agent combines an LLM with instructions, tools, and access that allow it to take action toward a goal.

If that’s still too technical, it’s helpful to think about the process of how an AI agent works. A basic version of the AI agent process looks something like this:

1. You go to whatever AI agent you’re using
2. You give that agent a request.
3. It interprets what you’re asking.
4. It determines what information or tools it needs.
5. It performs the available actions, based on what it can access (for example, different websites).
6. It evaluates what comes back.
7. It gives you a result or continues working through the task.

As an example, let’s say you’re asking an AI agent to gather pricing information for a flight so that you can go to your timeshare in Muncie, Indiana. The LLM will interpret your request and recognize that it needs to gather that information. It will use the resources it has access to pull that information and evaluate what it finds. Once that’s done, it’ll provide you with results based on the criteria set in your original prompt.

In short, you ask and AI agents answer to the best of their ability, whether you need help drafting quality content, refining paid ad copy, or some other tasks.

While the LLM is the intelligence at the center of the process, the tools around it determines what it can actually interact with. That distinction is where the difference between various types of AI agents starts to matter.

The Two Types of AI Agents You Should Know

AI terminology is still inconsistent, which is a big part of why it’s so hard to keep track of when you’re not fully immersed in the servitude of our robot overlords. 

That conundrum means there isn’t one universally accepted way to divide AI agents into neat categories. However, we can practically split them up into groups for sake of clarity:

• Chat interfaces
• Local AI agents

These two types of AI agents share a lot of the same underlying concepts, but they give users different levels of access, flexibility, and control.

Chat Interfaces: The AI Agents You Probably Already Know

If you’ve used ChatGPT or Claude, you already have direct experience with an actual AI agent. 

A chat interface gives you a ready-made environment where you can plug a prompt into a chat field and get answers. It looks incredibly simple on the surface, but this act lets you communicate with an LLM and use whatever capabilities the provider has built into that environment. These capabilities often include:

• Web research
• File uploads
• Document analysis

The appeal here is the minimal setup that makes AI agents extremely accessible. You open the application or browser, type what you need, provide any necessary information, and start working. You give made a request, and ChatGPT or whatever other chat interface you use responds in kind.

That level of investment may be enough for a lot of marketing tasks. You don’t need to build anything from scratch, and you don’t need deep technical expertise to get value from it.

That simplicity is also the limitation. You’re generally working inside the environment the provider created for you. The available tools, integrations, and behaviors are determined largely by what that platform supports.

For many users, that’s not a problem. In fact, those constraints can be useful. If your needs are straightforward, there may be no reason to introduce additional complexity.

Benefit Breakdown: Chat Interfaces

Like the sound of chat interfaces? Here’s a breakdown of some of the benefits these AI agents offer, ranked by the level of technical skill required to take advantage of them:

• No setup or installation (Low). Open a browser or an app and start. No API keys, no credentials, no environment to configure. Just a URL and a login when needed.
• Single request, single response (Low). Each turn is self-contained. Nothing runs in the background, and nothing persists once you close the window.
• Manual file handling (Low). You attach a document, it goes into context, you get an answer. No need for file system access, which means the agent isn’t reading from a drive or a database.
• Predictable cost per exchange (Low). One prompt, one completion. There's no loop that can quietly consume tokens for 20 minutes, so you always know about your investment.
• A human checks every output (Medium). Nothing moves forward until you approve it, so a bad response stops with you instead of feeding the next step.
• You manage the context window yourself (Medium). What's in the conversation is what the model sees. When it gets too long, you start a new chat. No retrieval layer to build or tune.
• Tight iteration loop on prompts (Medium). You see the result immediately and adjust the wording. Feedback is instant.
• Minimal attack surface (Medium). No OAuth scopes, no stored tokens, no write permissions to systems. Data goes in through the box you typed it into.
• Nothing to debug when the agent fails (High). There's no orchestration layer, no partial state, no half-finished run. A bad answer is just a bad answer and you can start off fresh with a new chat.
• The full reasoning sits in one transcript (High). Everything the model considered is visible and readable in order, which makes it auditable without extra tooling. 

If some of these benefits are too technical for your tastes, that’s not a problem. You don’t need to take advantage of all of them. As long as some of them make your life easier and your work better, then you’re on the right path.

Local AI Agents: More Access and Flexibility

While chat interfaces give you specific tools that you can access through a web browser, local AI agents move the experience closer to your own computer. Local AI agents are an application installed on your machine, where you can interact with on your laptop, desktop, or whatever device you have.

This experience feels very similar to opening Outlook, Slack, or another desktop tool. The local AI agent also has you type requests into an interface. The big difference is what the agent can access. 

For example, a local agent can be given access to select files and resources on your computer. This access eliminates the need to download and upload files into a browser-based interface. If you have a library of useful resources and documents, you can tie a local AI agent to that library to work directly with those files.

It’s also important to note that using a local AI agent does not automatically mean everything stays local. The application may live on your computer while still communicating with a cloud-hosted model or external API to process information. You control what the application can access on your device, but the underlying model may still exist outside the local machine. That distinction is why implementation and data access need to be considered carefully as organizations move into more advanced use cases.

Benefit Breakdown: Local AI Agents

Intrigued by the possibilities a local agent presents? These are some of the benefits users can enjoy with this type of AI agent, ranked by order of technical ability:

• Tool calling (Low). The model can invoke defined functions, such as search, calculator, lookup, instead of only producing text.
• File system read and write (Low). Opens files directly, edits them in place, saves output to a path. No upload and download cycle.
• Code execution in a sandbox (Medium). Writes and runs scripts to transform data, then uses the result. This feature is what turns "here's how you'd do it" into a finished file.
• Iteration until a condition is met (Medium). The agent loops on a task, checks the result, and tries again. It doesn't stop after one attempt the way a chat turn does.
• Persistent state across runs (Medium). Local agents can remember what they did last time, so the fortieth run doesn't repeat runs before it. 
• Authenticated system access (Medium). OAuth flows, API keys, scoped permissions against real services. A powerful feature, and the main thing to get right on security review.
• Scheduled- and event-driven triggers (Medium). Runs on a cron schedule or fires when something changes, with no one present to start it.
• Retry and fallback logic (High). Handles a timeout, a rate limit, or a malformed response without the whole run collapsing. This step constitutes most of the real engineering work.
• Multi-step orchestration (High). The local agent breaks a job into subtasks, runs some in parallel, passes results between them, sometimes delegating to separate subagents.
• Logging, evaluation, and guardrails (High). Traces of every action, tests that catch regressions, and hard limits on what the agent is permitted to do. Without this layer, you can't tell whether it's working.

Comparison Chart: How to Choose Between Chat Interface and Local AI Agent

Want an even simpler starting point on how to decide which type of agent is best for your needs? The easiest way to understand the difference is to compare them side by side. 

When Is a Chat Interface Enough?

Simply put, the right choice depends on the problem you’re trying to solve. There’s a tendency around new technology to assume that more sophisticated automatically means more valuable. That isn’t always true.
If your existing tools can solve the problem well, adding a more elaborate agent may just create extra work, cost, and maintenance. A chat interface may be perfectly adequate if:

• The task is clearly defined.
• The tool already has the capabilities you need.
• You don’t need extensive customization.
• You’re comfortable supplying the required files or information.
• The problem can be solved with a better prompt or a better workflow.

When Should You Consider a Local or Custom AI Agent?

There are situations where moving beyond a standard chat environment makes more sense. A local or more customized agent may be worth exploring when your workflow involves things like:

• Repeated interaction with local files or resources
• Multiple data sources
• More customized tools or integrations
• Complex, repeatable processes
• Parallel tasks
• Higher-volume use
• A need for greater consistency
• More control over how models and tools are configured

Once you move into that territory, technical expertise becomes increasingly important. There are agent frameworks available, sometimes called harnesses, that create a customizable environment around an LLM. You can plug different models into that environment and connect tools like OpenCode, OpenRouter, and Pi to create more custom setups.

The caveat is that you need someone who understands how to configure those technologies to move forward with this option. This person doesn’t need to be you, but it does have to be someone who can handle that added complexity.

Still Trying to Decide on an AI Agent? Start with What You Want to Accomplish.

The ever-expanding prospects of AI tools can make the latest technology feel like shiny objects. People see something exciting and want to immediately jump on it without judging if it makes sense or not. It may be a great new tool, but you don’t necessarily have to abandon your previous ones.

The same goes for AI agents. You don’t have to jump from casually using ChatGPT to building a multi-agent system. A more practical approach is to work your way up based on what the problem requires.

1. Start with a Real Problem

Don’t begin with “We need an AI agent” or “Let’s use the newest thing people talk about." Your tool of choice should be based on your problem, not the other way around. Use this process to give you something more concrete to evaluate:

• What takes too long?
• Where are people doing repetitive manual work?
• Where is information scattered across several systems?
• What process is difficult to scale?
• What outcome would actually matter to the business?

2. See if Your Existing AI Tools Can Handle It

Before adding another platform or development project, test whether the tools you already have can solve the problem. A standard chat interface may immediately do the trick. 

If ChatGPT, Claude, or whatever your interface of choice doesn’t quite work, don’t immediately assume it’s a bad fit. Sometimes better instructions, better context, or a better-defined workflow can make a massive difference with results. That extra testing makes for a much cheaper lesson than assuming it’s a bust and building something unnecessarily complex.

3. Explore a Local Agent When Access Becomes a Constraint

If the biggest limitation is the constant movement of files, context, or other resources between your computer and a browser-based tool, a local agent may be a logical next step.

Tools like Claude Code or similar desktop applications can provide an introduction to working with agents that have more direct access to approved local resources. If you get to the point where you need multiple integrations, customized tools, model selection, high-volume usage, or more complex orchestration, it’s worth involving someone who understands the technical trade-offs.

4. Measure Whether the Solution Actually Improved Anything

The act of implementing an AI agent is not a meaningful business outcome. You need to measure the impact it has on your work after you’ve worked agents into your processes. More useful questions to ask are:

• Did it reduce manual work, and by how much?
• Did it make information easier to access?
• Did it improve the consistency of a process?
• Did it shorten the time required to complete a task?
• Did it help your team spend more time on higher-value work?

Solve Business Problems Instead of Adopting AI for the Sake of It

AI agents can be incredibly flexible, but flexibility is only valuable when it helps solve the right problem. As new tools appear constantly and terminology changes, it’s more important to focus on what you understand: your business.

You don’t need to know every model, framework, tool, or technical acronym before you can participate in the AI agent conversation. You just need to be able to explain the problem you’re trying to solve.

• What’s happening today?
• What isn’t working?
• Where does the information live?
• What would a better process look like?

At Aztek, we prefer to work backward toward the right solution. Our goal is to understand your business problem before deciding which tactic or technology belongs in the solution, whether it’s a local AI agent, better use of a chat interface, or something that doesn’t need AI at all.

Start with the problem. Then choose the technology that actually helps solve it. If you need help with that, reach out to Aztek today so that we can discuss the best way to solve your digital business challenges.